@inproceedings{deng-etal-2026-structuring,
title = "Structuring the Unstructured: A Systematic Review of Text-to-Structure Generation with a Universal Evaluation Framework",
author = "Deng, Zheye and
Chan, Chunkit and
Zheng, Tianshi and
Fan, Wei and
Wang, Weiqi and
Song, Yangqiu",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.205/",
pages = "4533--4561",
ISBN = "979-8-89176-439-2",
abstract = "The evolution of AI systems toward agentic operation and context-aware retrieval necessitates transforming unstructured text into structured formats like tables, knowledge graphs, and charts. While such conversions enable critical applications from summarization to data mining, current research lacks a comprehensive synthesis of methodologies, datasets, and metrics. This systematic review examines text-to-structure techniques and the encountered challenges, evaluates current datasets and assessment criteria, and outlines potential directions for future research. We also introduce a universal evaluation framework for structured outputs, establishing text-to-structure as foundational infrastructure for next-generation AI systems."
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%0 Conference Proceedings
%T Structuring the Unstructured: A Systematic Review of Text-to-Structure Generation with a Universal Evaluation Framework
%A Deng, Zheye
%A Chan, Chunkit
%A Zheng, Tianshi
%A Fan, Wei
%A Wang, Weiqi
%A Song, Yangqiu
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F deng-etal-2026-structuring
%X The evolution of AI systems toward agentic operation and context-aware retrieval necessitates transforming unstructured text into structured formats like tables, knowledge graphs, and charts. While such conversions enable critical applications from summarization to data mining, current research lacks a comprehensive synthesis of methodologies, datasets, and metrics. This systematic review examines text-to-structure techniques and the encountered challenges, evaluates current datasets and assessment criteria, and outlines potential directions for future research. We also introduce a universal evaluation framework for structured outputs, establishing text-to-structure as foundational infrastructure for next-generation AI systems.
%U https://aclanthology.org/2026.aacl-long.205/
%P 4533-4561
Markdown (Informal)
[Structuring the Unstructured: A Systematic Review of Text-to-Structure Generation with a Universal Evaluation Framework](https://aclanthology.org/2026.aacl-long.205/) (Deng et al., AACL-IJCNLP 2026)
ACL